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In the information society, data explosion has led to more congestion in the core network, dampening the network performance. Random early detection (RED) is currently the standard algorithm for active queue management (AQM) recommended by the Internet Engineering Task Force (IETF). However, RED is particularly sensitive to both service load and algorithm parameters. The algorithm cannot fully utilize the bandwidth at a low service load, and might suffer a long delay at a high service load. This paper designs the reinforcement learning AQM (RLAQM), a simple and practical variant of RED, which controls the average queue length to the predictable value under various network loads, such that the queue size is no longer sensitive to the level of congestion. Q-learning was adopted to adjust the maximum discarding probability, and derive the optimal control strategy. Simulation results indicate that RLAQM can effectively overcome the deficiency of RED and achieve better congestion control; RLAQM improves the network stability and performance in complex environment; it is very easy to migrate from RED to RLAQM on Internet routers: the only operation is to adjust the discarding probability. [ABSTRACT FROM AUTHOR]

As the long term evolution (LTE) mobile users and transmission data increase, the load of the fronthaul network increases. In order to control the consumption of optical fiber resources, and prevent congestion under the premise of increasing data transmission, it is necessary to compress the data of the fronthaul network. In this paper, a data compression scheme of LTE-based fronthaul network is proposed. According to the characteristics of LTE baseband signals, discrete sine transform (DST) is applied to the time domain signals, and the transformed coefficients are partitioned according to the energy concentration characteristics. Bit allocation is performed in different blocks, and the coefficients of each block are quantized by Lloyd-Max quantizer. Finally, Huffman coding is carried out to improve the compression ratio under the condition that the error is allowed. The simulation results show that the proposed data compression scheme has good performance in both compression ratio (CR) and error vector magnitude (EVM). [ABSTRACT FROM AUTHOR]